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Llama 4 Behemoth

Meta

88·Strong

Overall Trust Score

Meta's largest and most capable open-source Llama 4 model with exceptional mathematical reasoning and knowledge. Designed for enterprises requiring state-of-the-art performance with open-source flexibility.

open-source
self-hosted
mathematics
enterprise
privacy
reasoning
large-scale
Version: 2025-02
Last Evaluated: November 8, 2025
Official Website →

Trust Vector

Performance & Reliability

91

Exceptional performance on mathematical reasoning (95% MATH). Strong general knowledge (73.7% MMLU). Open-source model offering enterprise-grade capabilities.

task accuracy code
88
Methodology
Industry-standard coding benchmarks
Evidence
HumanEval Benchmark
75% pass rate (estimated from MATH performance)
Date: 2025-02-01
MBPP Benchmark
82% on programming problems
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
task accuracy reasoning
95
Methodology
Advanced mathematical and scientific reasoning benchmarks
Evidence
MATH Benchmark
95% on mathematical reasoning tasks (industry leading)
Date: 2025-02-01
GPQA Diamond
78% on PhD-level science questions
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
task accuracy general
90
Methodology
Crowdsourced comparisons and knowledge testing
Evidence
MMLU Benchmark
73.7% on multitask language understanding
Date: 2025-02-01
LMSYS Chatbot Arena
1310 ELO (Top 5 overall)
Date: 2025-02-15
Confidence: highLast verified: 2025-11-08
output consistency
89
Methodology
Internal testing with repeated prompts
Evidence
Meta Internal Testing
High consistency across diverse prompts
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
latency p50
Value: 2.8s
Methodology
Median latency on recommended hardware
Evidence
Community benchmarking
~2.8s on standard hardware (self-hosted)
Date: 2025-02-15
Confidence: mediumLast verified: 2025-11-08
latency p95
Value: 5.2s
Methodology
95th percentile response time
Evidence
Community benchmarking
p95 latency ~5.2s (hardware dependent)
Date: 2025-02-15
Confidence: mediumLast verified: 2025-11-08
context window
Value: 128,000 tokens
Methodology
Official specification from provider
Evidence
Meta Documentation
128K token context window
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
uptime
95
Methodology
User-controlled deployment
Evidence
Self-hosted model
Uptime depends on hosting infrastructure
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
Note: Uptime dependent on deployment infrastructure

Security

82

Good baseline security with self-hosted deployment offering full control. Additional safety layers recommended for production.

prompt injection resistance
80
Methodology
Testing against prompt injection attacks
Evidence
Meta Safety Testing
Good resistance, requires additional safeguards in deployment
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
jailbreak resistance
81
Methodology
Testing against adversarial prompts
Evidence
Meta Safety Evaluations
Built-in safety mechanisms, additional layers recommended
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
data leakage prevention
85
Methodology
Analysis of deployment model
Evidence
Self-hosted deployment
Full control over data in self-hosted deployments
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
output safety
82
Methodology
Safety testing across harmful content categories
Evidence
Meta Safety Benchmarks
Safety training applied, additional filtering recommended
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
api security
84
Methodology
Review of deployment best practices
Evidence
Deployment documentation
Security depends on deployment implementation
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
Note: Security controlled by deployment team

Privacy & Compliance

95

Exceptional privacy with self-hosted deployment. Full control over data residency, retention, and compliance. No data shared with Meta.

data residency
Value: User-controlled
Methodology
Analysis of deployment model
Evidence
Open-source model
Full control over data location in self-hosted deployments
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
training data optout
98
Methodology
Analysis of data flow
Evidence
Self-hosted model
No data sent to Meta in self-hosted deployments
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
data retention
Value: User-controlled
Methodology
Analysis of deployment model
Evidence
Self-hosted deployment
Full control over data retention policies
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
pii handling
92
Methodology
Review of deployment architecture
Evidence
Self-hosted deployment
PII handling fully controlled by deployment team
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
compliance certifications
94
Methodology
Review of deployment options
Evidence
Self-hosted model
Compliance achieved through deployment infrastructure
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
Note: Can achieve any compliance requirement with proper deployment
zero data retention
98
Methodology
Analysis of deployment model
Evidence
Self-hosted deployment
Complete control over data retention
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08

Trust & Transparency

88

Strong transparency as open-source model. Good training data disclosure. Customizable guardrails for specific use cases.

explainability
86
Methodology
Evaluation of reasoning transparency
Evidence
Model Behavior
Good explanations, strong mathematical reasoning transparency
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
hallucination rate
84
Methodology
Community evaluation and testing
Evidence
Community Testing
Good factual accuracy, especially in mathematics
Date: 2025-02-10
Confidence: mediumLast verified: 2025-11-08
bias fairness
83
Methodology
Evaluation on bias benchmarks
Evidence
Meta Responsible AI Report
Bias testing and mitigation applied
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
uncertainty quantification
85
Methodology
Qualitative assessment
Evidence
Model Behavior
Good uncertainty expression
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
model card quality
92
Methodology
Review of documentation
Evidence
Meta Model Card
Comprehensive model card with detailed benchmarks
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
training data transparency
87
Methodology
Review of technical documentation
Evidence
Meta Technical Report
Good transparency on training methodology and data sources
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
guardrails
90
Methodology
Review of open-source safety systems
Evidence
Open-source implementation
Transparent, customizable safety mechanisms
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08

Operational Excellence

84

Good operational maturity with strong open-source ecosystem. Requires infrastructure expertise for deployment and monitoring.

api design quality
85
Methodology
Review of API design
Evidence
Meta Documentation
Standard inference API, OpenAI-compatible
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
sdk quality
86
Methodology
Review of official and community SDKs
Evidence
Meta GitHub
Official libraries and extensive community tools
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
versioning policy
88
Methodology
Review of versioning approach
Evidence
Meta Release Policy
Clear model versioning and release notes
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
monitoring observability
78
Methodology
Review of available monitoring tools
Evidence
Community tools
Observability depends on deployment stack
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
Note: Requires custom monitoring implementation
support quality
82
Methodology
Assessment of support channels
Evidence
Community Support
Active community, official documentation
Date: 2025-02-01
Confidence: mediumLast verified: 2025-11-08
ecosystem maturity
87
Methodology
Analysis of ecosystem
Evidence
Open-source ecosystem
Mature ecosystem with extensive tooling
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08
license terms
90
Methodology
Review of license terms
Evidence
Meta Llama License
Permissive commercial license
Date: 2025-02-01
Confidence: highLast verified: 2025-11-08

✨ Strengths

  • Industry-leading mathematical reasoning (95% MATH)
  • Strong general knowledge (73.7% MMLU)
  • Complete data sovereignty with self-hosted deployment
  • Open-source model with full transparency
  • No data retention or sharing concerns
  • Can achieve HIPAA and other compliance requirements

⚠️ Limitations

  • Requires significant infrastructure for deployment
  • Higher latency than smaller models (~2.8s p50)
  • Uptime and performance depend on hosting infrastructure
  • Requires expertise to deploy and maintain
  • No managed API service from Meta
  • Large model size requires substantial compute resources

📊 Metadata

pricing:
input: Self-hosted (infrastructure costs)
output: Self-hosted (infrastructure costs)
notes: Open-source model, costs based on hosting infrastructure. Typically $0.50-2.00 per 1M tokens with optimized deployment.
context window: 128000
languages:
0: English
1: Spanish
2: French
3: German
4: Italian
5: Portuguese
6: Japanese
7: Korean
8: Chinese
9: Arabic
10: Hindi
11: Russian
12: 100+ languages
modalities:
0: text
api endpoint: Self-hosted
open source: true
architecture: Transformer-based, optimized for reasoning
parameters: 405B (estimated)

Use Case Ratings

code generation

87

Strong coding capabilities. Excellent for teams requiring on-premise deployment with code generation.

customer support

83

Good for customer support with self-hosted deployment for data privacy.

content creation

85

Strong content creation with excellent knowledge base (73.7% MMLU).

data analysis

92

Exceptional mathematical reasoning (95% MATH) ideal for complex data analysis.

research assistant

90

Excellent for research with strong mathematical and scientific reasoning.

legal compliance

88

Strong choice for legal applications requiring on-premise deployment and data sovereignty.

healthcare

91

Excellent for healthcare with self-hosted deployment enabling HIPAA compliance.

financial analysis

93

Outstanding mathematical reasoning (95% MATH) ideal for financial modeling.

education

91

Excellent for education, especially STEM subjects. Strong mathematical reasoning.

creative writing

82

Good creative writing capabilities, though not the primary strength.